Chance-Constrained Optimal Distribution Network Partitioning to Enhance Grid Resilience

TL;DR

This paper develops a chance-constrained optimization method to partition distribution networks into resilient islands under uncertainty, using sample average approximation to improve planning for grid reliability.

Contribution

It introduces a novel chance-constrained formulation for distribution network partitioning that accounts for load and renewable uncertainties, with an efficient solution approach.

Findings

The method effectively identifies resilient sub-networks under uncertainty.

Partitioning adapts with different risk budgets, enhancing flexibility.

SAA provides high-quality solutions with modest computational effort.

Abstract

This paper formulates a chance-constrained optimal distribution network partitioning (ODNP) problem addressing uncertainties in load and renewable energy generation; and presents a solution methodology using sample average approximation (SAA). The objective is to identify potential sub-networks in the existing distribution grid; that are likely to survive as self-adequate islands if supply from the main grid is lost. {This constitutes a planning problem.} Practical constraints like ensuring network radiality and availability of grid-forming generators are considered. Quality of the obtained solution is evaluated by comparison with: a) an upper bound on the probability that the identified islands are supply-deficient, and b) a lower bound on the optimal value of the true problem. Performance of the ODNP formulation is illustrated on a modified IEEE 37-bus feeder. It is shown that the network flexibility is well utilized; the partitioning changes with risk budget; and that the SAA method is able to yield good quality solutions with modest computation cost.